A system and method of the present disclosure provides a region based error diffusion algorithm or function for reducing artifacts in images. The system and method utilizes knowledge of a region of interest (ROI) in an image and applies effects like dithering or error diffusion to the area or region that shows the artifacts, e.g., a banding phenomenon. The system and method provide for defining a region in at least one first image, the defined region having at least one artifact, tracking the defined region to at least one second image, and applying an error diffusion function to the defined region to mask the at least one artifact in the at least one first and second images.
Legal claims defining the scope of protection, as filed with the USPTO.
1. A method for masking artifacts in images, the method comprising: defining a region in at least one first image, the defined region having at least one artifact; tracking the defined region to at least one second image; and applying an error diffusion function to the defined region to mask the at least one artifact in the at least one first and second images comprising adding a masking signal to at least one block in at least one first image and at least one second image based on a block size of pixels.
2. The method as in claim 1 , wherein the applying step further comprises: selecting a block size of pixels of an image; adding the masking signal to the at least one block; determining a quantization error for the at least one block in the image; and distributing the quantization error to neighboring blocks.
3. The method of claim 2 , further comprising, after the distributing step, encoding the at least one first and second images with a compression function.
4. The method as in claim 3 , wherein the compression function is lossy.
5. The method as in claim 2 , wherein the adding a masking signal step comprises: determining a distance of at least one pixel in the at least one block to a boundary of the defined region; and assigning a value to a masking signal associated to the at least one pixel based on the determined distance.
6. The method as in claim 2 , wherein the masking signal is a noise signal.
7. The method as in claim 2 , wherein the determining the quantization error step further comprises: truncating each pixel in the at least one block; determining a quantization error for each pixel; and summing the quantization error of each pixel in the at least one block.
8. The method as in claim 1 , wherein the tracking step further comprises: generating a binary mask for the defined region of the at least one first image; and projecting the binary mask to the at least one second image to track the defined region.
9. The method as in claim 8 , wherein the projecting step further comprises estimating motion of the defined region from the at least one first image to the at least one second image.
10. The method as in claim 9 , wherein the estimating step is performed by an affine motion model.
11. The method as in claim 8 , wherein the generating step further comprises transforming the defined region into a larger region to capture features of the at least one first image to be tracked.
12. The method as in claim 1 , wherein the defining a region step is performed manually by outlining the region or automatically by a detection function.
13. A system for masking artifacts in images, the system comprising: a tracking module configured for tracking a defined region in at least one first image to at least one second image, the defined region having at least one artifact; and an error diffusion module configured for applying an error diffusion function to the defined region to mask the at least one artifact in the at least one first and second images comprising adding a masking signal to at least one block in at least one first image and at least one second image based on a block size of pixels.
14. The system as in claim 13 , further comprising a user interface configured for defining the region in the at least one first image.
15. The system as in claim 13 , further comprising an encoder configured for encoding the at least one first and second images with a compression function.
16. The system as in claim 13 , wherein the error diffusion module further comprises a signal generator configured for generating a masking signal to be applied to at least one image; wherein the error diffusion module is further configured for selecting a block size of pixels of the at least one image, determining a quantization error for the at least one block in the at least one image; and distributing the quantization error to neighboring blocks.
17. The system as in claim 16 , wherein the signal generator is configured to generate a noise signal.
18. The system as in claim 16 , wherein the error diffusion module is further configured for determining a distance of at least one pixel in the at least one block to a boundary of the defined region; and assigning a value to a masking signal associated to the at least one pixel based on the determined distance.
19. The system as in claim 16 , wherein the error diffusion module further comprises a truncation module configured to truncate each pixel in the at least one block, determine a quantization error for each pixel and sum the quantization error of each pixel in the at least one block.
20. The system as in claim 13 , wherein the tracking module further comprises a mask generator configured for generating a binary mask for the defined region of the at least one first image; wherein the tracking module is further configured for projecting the binary mask to the at least one second image to track the defined region.
21. The system as in claim 20 , wherein the tracking module further comprises a tracking model configured to estimate motion of the defined region from the at least one first image to the at least one second image.
22. The system as in claim 21 , wherein the tracking model is an affine motion model.
23. The system as in claim 20 , wherein the tracking module is further configured for transforming the defined region into a larger region to capture features of the at least one first image to be tracked.
Cooperative Patent Classification codes for this invention. Click any code to explore related patents in that topic.
June 19, 2007
June 4, 2013
Browse 5M+ US patents with plain-English claim translations and AI-generated analysis.